Abstract
We describe an efficient method for accurate estimation of the score function of a random variable, which can be regarded as an extension of the FFT-based fast density estimation method of Silverman (1982), and which scales no more than linearly with the sample size. We demonstrate the utility of our approach in a real-life ICA problem involving the separation of eight sound signals, where better results are observed than using state-of-the-art ICA methods.
This research is supported by the Dutch Technology Foundation STW, applied science division of NWO, and the technology programme of the Ministry of Economic Affairs, project AIF 4997.
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Vlassis, N. (2001). Fast Score Function Estimation with Application in ICA. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_76
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DOI: https://doi.org/10.1007/3-540-44668-0_76
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